Algorithms for Natural Language Processing Lecture 18b: Semantic Roles
Semantics Roadmap • You should already have been convinced that grammatical structure is an important aspect of language • Now we are discussing semantics or meaning • Up until today, we have talked about meaning as something that individual words have (whether in isolation or in context) • So far today, we have talked about representing the meanings of propositions/sentences in meaning representation languages • Now, we are going to discuss an enhancement to this view, the notion that individual noun phrases can be characterized as having roles relative to a predicate or frame
• Noah built an ark out of gopher wood. • He loaded two of every animal onto the ark. • Noah piloted the ark into stormy weather. • When the skies cleared, all rejoiced.
• Noah 1 built an ark 2 out of gopher wood. • He 1 loaded two of every animal onto the ark 2 . • Noah 1 piloted the ark 2 into stormy weather. • When the skies 3 cleared, all 4 rejoiced.
Paraphrase • Noah built an ark out of gopher wood. • An ark was built by Noah. It was made from gopher wood. • Noah constructed an ark with wood from a gopher tree. • Using gopher wood, Noah managed to put together an ark. • Noah built an ark. • …
Traditional Semantic Roles In the linguistics literature, one sees a number of common terms for • semantic roles – Agent – Patient – Theme – Force – Experiencer – Stimulus – Recipient – Source – Goal – etc. These have their place, and are useful to know if you want to understand • what a semantic role is, but are not widely used in NLP In NLP, we tend to use finer-grained (and sometimes cryptically named) • semantic role labels
Traditional Semantic Roles • David threw the midterms from Pausch Bridge to the hillside below . – David —agent – the midterms —theme – Pausch Bridge —source – the hillside below —goal
Neo-Davidsonian Representation • David threw the midterms from Pausch Bridge to the hillside below – THROW (David, midterms, PauschBridge, hillside) – ∃ e THROW ( e ) ∧ AGENT ( e , David) ∧ THEME ( e , midterms) ∧ SOURCE ( e , PauschBridge) ∧ GOAL ( e , hillside) • The midterms were thrown from Pausch Bridge – THROW (midterms, PauschBridge) – ∃ e THROW ( e ) ∧ THEME ( e , midterms) ∧ SOURCE ( e , PauschBridge)
Semantic Role Labeling Input : a sentence, paragraph, or document Output : for each predicate*, labeled spans identifying each of its arguments. *Predicates are sometimes identified in the input, sometimes not.
Predicates • Noah built an ark out of gopher wood. • An ark was built by Noah. It was made from gopher wood. • Noah constructed an ark with wood from a gopher tree. • Using gopher wood, Noah managed to put together an ark.
Predicates and Arguments • Noah built an ark out of gopher wood. • An ark was built by Noah. It was made from gopher wood. • Noah constructed an ark with wood from a gopher tree. • Using gopher wood, Noah managed to put together an ark.
Breaking, Eating, Opening John broke the window. • The window broke. • John is always breaking things. • The broken window testified to John’s malfeasance. • Eat! • We ate dinner. • We already ate. • The pies were eaten up quickly. • Our gluttony was complete. • Open up! • Someone left the door open. • John opens the window at night. •
Introducing PropBank • Corpus (PTB) with propositions annotated – Predicates (verbs) – Arguments (semantic roles) • Semantic roles are Arg0, Arg1, etc., each with a description – Arg0 is typically the most agent-like argument – Labels for other arguments are somewhat arbitrary
“Agree” in PropBank • arg0: agreer • arg1: proposition • arg2: other entity agreeing • The group agreed it wouldn ’ t make an offer. • Usually John agrees with Mary on everything
“Fall (move downward)” in PropBank • arg1: logical subject, patient, thing falling • arg2: extent, amount fallen • arg3: starting point • arg4: ending point • argM-loc: medium • Sales fell to $251.2 million from $278.8 million. • The average junk bond fell by 4.2%. • The meteor fell through the atmosphere, crashing into Cambridge.
FrameNet • A frame is a schematic representation of a situation involving various participants, and other conceptual roles • In FrameNet, frames—not verbs—are first-class citizens – To a first approximation, verbs that relate to the same situation belong to the same frame – Roles are given fine-grained labels that are specific to the frame, but not the verb – Frames can center around words other than verbs
change_position_on_a_scale Core roles A TTRIBUTE scalar property that the I TEM possesses D IFFERENCE distance by which an I TEM changes its position F INAL _ STATE I TEM ’s state after the change F INAL _ VALUE position on the scale where I TEM ends up I NITIAL _ STATE I TEM ’s state before the change I NITIAL _ VALUE position on the scale from which the I TEM moves I TEM entity that has a position on the scale V ALUE _ RANGE portion of the scale along which values of A TTRIBUTE fluctuate Some non-core roles ... D URATION length of time over which the change occurs S PEED rate of change of the value G ROUP the group in which an I TEM changes the value of an A TTRIBUTE
• Verbs : advance, climb, decline, decrease, diminish, dip, double, drop, dwindle, edge, explode, fall, fluctuate, gain, grow, increase, jump, move, mushroom, plummet, reach, rise, rocket, shift, skyrocket, slide, soar, swell, swing, triple, tumble • Nouns : decline, decrease, escalation, explosion, fall, fluctuation, gain, growth, hike, increase, rise, shift, tumble • Adverb : increasingly
Demo https://framenet.icsi.berkeley.edu/fndrupal/
How Can We Build an SRL System? (1) Parse (2) For each predicate word in the parse: For each node in the parse: Classify the node with respect to the predicate
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